This site is about everything digital, giving an update on new things as I learn

Category: AI

Dr. Kai-Fu Lee is the chairman and CEO of Sinovation Ventures, a China based tech focused investment firm. Previous to becoming a full-time investor, Lee held positions at Google, Microsoft and Apple. A large part of that career, Lee spent working on data and Artificial Intelligence (‘AI’), both in the US and in China. In “AI Superpowers – China, Silicon Valley and the New World Order” Lee bundles his experiences and insights to describe the progress that China and the US have made and are making in the field of AI.

AI Superpowers contains a heap of valuable insights as well as predictions about the impact of technology power that both the US and China have been racking up. These are the main things that I took away from reading AI Superpowers:

US and China, contrasting cultures – Lee starts the book by writing about the contrasts in business culture between the US and China: “China’s startup culture is the yin to Silicon Valley’s yang: instead of being mission-driven, Chinese companies are first and foremost market-driven.” Lee goes on to explain that the ultimate goal of Chinese companies is “to make money, and they’re willing to create any product, adopt any model, or go into any business that will accomplish that objective.” This mentality help to explain the ‘copycat’ attitude that Chinese companies have had historically. Meituan, for example, is a group-discount website which sells vouchers from merchants for deals which started as the perfect counterpart of US-based Groupon.

“Online-to-Offline” (‘O2O”) – O2O describes the conversion of online actions into offline services. Ride-sharing services like Uber and Lyft are great examples of the new O2O model. In China, Didi copied this model and tailored it to local conditions. Didi was followed by other O2O plays such as Dianping, a food delivery service which subsequently merged with the aforementioned Meituan company, and Tujia, a Chinese version of Airbnb. Lee also mentions WeChat and Alipay, describing how both companies completely overturned China’s all-cash economy. More recently, bike-sharing startups Mobike (see Fig. 1 below) and ofo which supplied tens of millions of internet-connected bicycles, distributing them across them about major Chinese cities and now across the globe.

China catching up quickly in the AI department – Having read the story of image recognition algorithm ResNet, and how its inventors moved from Microsoft to join AI startups in China, I can see how China as a country is quickly catching up with the technology stalwart that is Silicon Valley. One of these image recognition startups, Face +++, has quickly become a market leader in face / image recognition technology, leapfrogging the likes of Google, Microsoft and Facebook along the way.

The four waves of AI – In AI Superpowers, Lee argues that what he calls the “AI revolution” will not happen overnight. Instead, AI will wash over us in four waves: internet AI, business AI, perception AI, and autonomous AI (see Fig. 2 below). This part of the book really struck a chord with me, as it brings to life how AI is likely to evolve over the coming years, both in terms of practical applications and use cases.

Main learning point: I’d highly recommend “AI Superpowers” to anyone interested in learning more about how China and the US are furthering the development of AI and the impact of this development on our daily lives.

First wave: Internet AI – Internet AI is largely about using AI algorithms as recommendation engines: systems that learn our personal preferences and then serve up content hand-picked for us. Toutiao, sometimes called “the Buzzfeed of China”, is a great example of this first wave of AI; its “editors” are algorithms.

Second wave: Business AI – First wave AI leverages the fact that internet users are automatically labelling data as they browse. Business AI, the second wave of AI, takes advantage of the fact that traditional companies have also been automatically labelling huge quantities of data for decades. For instance, insurance companies have been covering accidents and catching fraud, banks have been issuing loans and documenting repayment rates, and hospitals have been keeping records of diagnoses and survival rates. Business AI mines these data points and databases for hidden correlations that often escape the naked eye and the human brain. RXThinking, an AI based diagnosis app, is a good example in this respect.

Third wave: Perception AI – Third wave AI is all about extending and expanding this power throughout our lived environment, digitising the world around us through the proliferation of sensors and smart devices. These devices are turning our physical world into digital data that can then be analysed and optimised by deep-learning algorithms. For example, Alibaba’s City Brain is digitising urban traffic flows through cameras and object-recognition.

“Managing products of the future” came up when I was thinking of a suitable title for a piece about products that look and feel very different to most products that we see today. Products such as driverless cars and voice assistants popped into my head as examples of products that are likely to dominate our daily lives before we know it.

However, these products are here already and I’m keen to look at if and how this does affect the role and focus of product management.

Will we manage products differently when the user interface of these products changes? Do we need to think differently about our products when data becomes the main output? Will customer needs and expectations evolve? If so, how? These and other questions I will start thinking about; considering the nature of machine learning, different product scenarios and their impact on the role of the product manager.

Taken from: https://robertmerrill.wordpress.com/2009/04/15/the-future-is-already-here/

It’s easy to get swept up by the hype surrounding AI and products based on machine learning, and to start feeling pretty dystopian about the future. But how much will actually change from a product management point of view? People will continue to have specific needs and problems. As product managers, we’ll continue to look at best ways of solving these problems. Granted, the nature of people’s needs and problemx will evolve, as it has always done, but this won’t alter the problem solving and people centric nature of product management.

To illustrate this, let’s look at some AI-base products and the customer needs and problems that they’re aiming to solve: Google Photos, Sonos One and Eigen Technologies.

Google Photos’ strap-line is “One home for all your photos – organised and easy to find”. Over the coming months, Google Photos will roll out the following features:

Using facial recognition, Google Photos will know who’s in a picture and will offer a one-tap option to share it with the person in question – provided that this person is in your phone’s contact list, Google Photos will have learned this person’s face. If that person appears in multiple images, Google Photos will even suggest to share all of them in one go.

Automated image editing suggestions, Google Photos will suggest different corrections based on the look and quality of the image. For example, if there issues with the brightness of the image, Google Photos will automatically display a “Fix brightness” suggestion.

With these new features, Google Photos aim to address customer needs with regard to sharing pictures and improving image quality respectively. These needs aren’t new per se, but the ‘intelligent’ aspect of Google Photos’ approach is.

The Sonos One is entirely controlled by voice. The speaker works fully with Amazon Alexa, which means that if you’ve got an Amazon Alexa compatible device, you can control your Sonos sound system through Amazon Alexa. Because Alex is a native app within the Sonos platform, you don’t even need to have an external Amazon device – i.e. Echo or the Dot – installed to control your Sonos One speaker. The installation of the Alexa mobile app will be enough.

The integration with the Amazon’s Alexa voice assistant is a logical next step within Sonos’ mission to “empower everyone to listen better” and makes it easier for people to control the music they listen to. Granted, the user interface of Sonos One is different to other product; it doesn’t have buttons, for example. However, it still is a product like any other in a sense that it delivers tangible value to customers by solving their music listening needs.

“Turn your documents into data” is London and New York based Eigen Technologies’ mission statement. The company enables the mining of documents for specific data. For example, if you work for a mortgage lender and are looking to make a decision about the credit worthiness of a home, Eigen’s data extraction technology helps to quickly pull out key ‘decision inputs’ from a number of – often very lengthy – property documents.

The way in which Eigen Technologies use machine learning algorithms, is ultimately to improve the speed and quality of decision making. Even though the underlying technology is based on machine learning, the outcome is very much like that of any other product: a clear user interface which shows the relevant document data that a user is interested in and needs to make decisions.

Main learning point: AI and machine learning based products will no doubt change the ways in which we interact with products and what we expect of them. However, existing examples such as Google Photos and Sonos One already show that the core of the product manager’s role will remain unchanged: building the right product for the right people and building it right!

These smart glasses connect to a feed which taps into China’s state database to detect out potential criminals using facial recognition. Officers can identify suspects in a crowd by snapping their photo and matching it to their internal database.

Wrong360 is a Chinese peer-to-peer lending app which aims to make obtaining a loan as simple as possible. When users of the Wrong360 app enter the amount of loan, period, and purpose, the platform will automatically do the match and output a list of banks or credit agencies corresponding to the users’ requests. On the list, users can find the institution names, products, interests rate, gross interests, monthly payment, and the available periods, etc. Applying for a loan can done fully online, and the app uses facial recognition as part of the loan application process.

Product 3 — Security camera

Security cameras in public places to help police officers and shopkeepers by improved ways of face matching. Traditionally, face matching is based on trait description of someone’s facial features and the special distance between these features. Now, by extracting the geometric descriptions of the parts of the eyes, nose, mouth, chin, etc. and the structural relationship between them, search matching is performed with the feature templates stored in the database. When the similarity exceeds the set threshold, the matching results are shared.

Whether it’s “SenseTotem” — which is being used for surveillance purposes — or “SensePhoto” — which uses facial recognition technology for messaging apps and mobile cameras — it all comes from the same company: SenseTime.

The company has made a lot of progress in a relatively short space of time with respect to artificial intelligence based (facial) recognition. The Chinese government has been investing heavily in creating an ecosystem for AI startups, with Megvii as another well known exponent of China’s AI drive.

A project with the code name “Viper” is the latest in the range of products that SenseTime is involved. I’m intrigued and slightly scared by this project which is said to focus on processing thousands of live camera feeds (from CCTV, to traffic cameras to ATM cameras), processing and tagging people and objects. SenseTime is rumoured to want to sell the Viper surveillance service internationally, but I can imagine that local regulations and data protection rules might prevent this kind of ‘big brother is watching you’ approach to be rolled out anytime soon.

Main learning point: It seems that SenseTime is very advanced with respect to facial recognition, using artificial intelligence to combine thousands of (live) data sources. You could argue that SenseTime isn’t the only company building this kind of technology, but their rapid growth and technological as well as financial firepower makes them a force to be reckoned with. That, in my mind, makes SenseTime very special indeed.

Artificial Intelligence (‘AI’) has rapidly become yet another buzzword in the tech space and I’m therefore always on the lookout for AI based applications which add actual customer value. StatusToday could that kind of app:

My quick summary of StatusToday before using it – I think Status Today provides software to help manage teams of employees, I suspect this product is geared towards HR people.

How does StatusToday explain itself in the first minute – “Understand your employees” is the strapline that catches my eye. Whilst not being entirely clear on the tangible benefits Status Today delivers on, I do get that it offers employee data. I presume that customers will have access to a data portal and can generate reports.

What does StatusToday do (1)? – StatusToday analyses human behaviour and generates a digital fingerprint for individual employees. The company originally started out with a sole focus on using AI for cyber security, applying designated algorithms to analyse internal online comms, detecting behavioural patterns in comms activity and quickly spotting any abnormal activity or negligence. For example, ‘abnormal file exploration’ and ‘access from unusual locations’ are two behaviours that StatusToday will be tracking for its clients.

What does StatusToday do (2)? -StatusToday has since started offering more generic employee insights services. By plugging into a various online tools companies may use, Google and Microsoft for example, StatusToday will start collecting employee activity data. This will help companies in getting better visibility of employee behaviour as well as making the processes around data access and usage more efficient.

It makes me wonder to what extent there’s a “big brother is watching you element” to StatusToday’s products and services. For example, will the data accessible through StatusToday’s “Live Dashboard” (eventually) make it easier for companies to punish employees if they’re spending too much time on Facebook!?

Main learning point: I can see how StatusToday takes the (manual) pain out of monitoring suspicious online activity and helps companies to preempt data breaches and other ‘anomalies’.

It isn’t often that one of the apps that I use on a regular basis attracts a large round of funding but it happened recently with Receipt Bank, a London based started which “makes your bookkeeping, faster, easier and more efficient.” Last month, Receipt Bank received a Series B investment worth $50 million from New York basedInsight Venture Partners.

Receipt Bank, which started in 2010, targets accountants, bookkeepers and small businesses. It offers them an online platform through which users can submit their invoices, receipts, and bills by taking a picture and uploading it through Receipt Bank’s mobile app (see Fig. 1), desktop app (see Fig. 2), or an email submission. Receipt Bank’s system then automatically extracts relevant data, sorts and categorises it. Apart from viewing your processed expenses online, Receipt Bank also publishes everything to the user’s accounting software of choice, FreshBooks or Xero for example.

Fig. 1 – Screenshot of Receipt Bank iOS app

Fig. 2 – The entry in Receipt Bank for one of my receipts

Given that I’ve been using Receipt Bank for a while now; instead of just reviewing existing functionality, I’ve also had a think about how I’d use a $50m war chest to further build out the Receipt Bank product:

Faster! Faster! Faster! – When I started using Receipt Bank last year, I emailed the customer support team enquiring about the wait between submitting a picture of a receipt and it being “ready for export”. I got a friendly reply explaining that “we ask for a maximum of 24 hours to process items, but we are usually much faster than that.” The customer support adviser also explained that “the turnaround time also depends on the number of items waiting to be processed by the software and also their quality.” I’m sure Receipt Bank uses some form of machine-learning, algorithms to automatically interpret and categorise the key data fields from the picture of a receipt. As the field of Artificial Intelligence continues to evolve, I expect Receipt Bank to be able to – eventually – process receipts and invoices within seconds, with no need for the user to add or edit any info processed. Because I envisage machine learning to be the core driver of Receipt Bank’s proposition, I suggest spending at least half of its latest investment on AI technology and engineers specialised in machine learning.

Not just tracking my bills and invoices – Yes, everybody is jumping on the chatbot wagon (and some of the results are frankly laughable). However, I do believe that if Receipt Bank can learn a sufficient amount about its customers and their spending and accounting behaviours, it will be able to provide them with tailored advice and predictions. For example, if I pay my supplier in China a fixed amount per month to keep my stock up, I’d like to ask Receipt Bank’s future “Expense Assistant” how my supplier payments will be affected if there’s massive volatility in the exchange rate between the British Pound and the Chinese Yuan. Similarly, when I look at most of today’s finance departments, the people in these teams seem to spend on matching the right payments received to the relevant invoice(s) sent out. I realise that the machine learning around multiple invoices wrapped into a single payment is easier said than done, but I don’t think it will be impossible and the $25m investment into AI (see point 1. above) should help massively.

What if the days of paper bills are numbered!? – Now that I’ve effectively spent $25m on AI technology, I’ve got $25m left. The first thing I’d do with this remaining money is to prepare for scenarios where invoices or receipts are no longer issued on paper but provided orally. At the moment, capability likeAlexa Expense Tracker is mostly used for personal expenses, but I do envisage a future where people use Alexa or Siri to add and track their expenses. Given that voice technology is still very much in its infancy, I suggest restricting Receipt Bank’s investment into this area to a no more than $1m.

Integrate more (and please don’t forget about Asia) – If I were Receipt Bank I’d probably use about $10m of the remaining fund to enter new geographies and integrate with additional systems. For example, I like how Sage’s Pegg hooks into any expenses you record on your mobile, whether it’s via Slack, Facebook, Skype, WhatsApp, etc. I don’t know whether Receipt Bank is looking to enter the Asian market, but I feel there’s great opportunity to integrate with messenger apps like WeChat and Hike, without spending more than $2m on development and marketing. Also, integrating with payment processors, like Finsync did recently with Worldpay, is an integration avenue worth considering!

But don’t forget about the current product! – I feel Receipt bank would be remiss if it were to forget about improving its current platform, both in terms of functionality and user experience. For example, I can’t judge how well Receipt Bank does in retaining its customers, but I feel there are a number of ways in which it can make the existing product ‘work harder’ (see Fig. 3 below). In my experience, some of my proposed improvements and features shouldn’t break the bank. By spending about $1m on continuous improvements over a number of years, Receipt Bank should have at least $20m left in the bank, as a buffer for difficult times and any new opportunities that might arise during the product lifecycle.

Some touches ofgamification – I’d argue that the longevity of the relationship between Receipt Bank and an individual user is determined by how often the users uploads bills onto the platform. I assume that most users will most probably not view managing their expenses as fun, I think it would be good to look at ways to make the experience more fun. For example, I could get a gold star from my accountant once I’ve successfully synced my month’s expenses into my accounting system. I feel that there’s plenty of room to reinforce the current gamification elements that Receipt Bank uses. For example, the message that Receipt Bank managed to save 27 minutes of my time doesn’t really do it for me (see Fig. 4 below). Instead, the focus could be on the productivity gain that I’ve made for billable work (if I’m a freelancer for example).

Better progress and status updates – Even if it does continue to take up to 24 hours. to categorise and process my expenses, it would be great if Receipt Bank could make its “in progress” status more intuitive and informative.

Clearer and stronger calls to action – For example, I can see that I’m not making the best use of my Receipt Bank subscription (see Fig. 5 below). However, there are no suggestions on specific actions I can take to get more value from my Receipt Bank plan.

Fig. 4 – Screenshot my Receipt Bank usage

Fig. 5 – Screenshot of my Receipt Bank “Usage summary”

Main learning point: Having thought about Receipt Bank’s current product offering, and my understanding of their target market, I suggest investing a good chunk of the recent investment into optimising the machine learning algorithms in such a way that both processing speed and accuracy are significantly increased. By doing this, the customer profile and behavioural data generated, will create additional opportunities to further retain customers and offer adjacent products and services.

When I first heard about Toutiaou I thought it might be just another news app, this coming one from China. I learned, however, very quickly that Toutiaou is much more than just a news app; at the time of writing, Toutiao has more than 700 million users in total, with ore than 78 million users reading over 1.3 billion articles on a daily basis.

Toutiao, known officially as Jinri Toutiao, which means “Today’s Headlines”, has a large part of its rapid rise to its ability to provide its users with a highly personalised news feed. Toutiao is a mobile platform that use machine learning algorithms to recommend content to its users, based on previous content accessed by users and their interaction with the content (see Fig. 2).

Fig. 2 – Screenshot of Toutiao iOS app

I identified a number of elements that contribute to Toutiao’s success:

AI and machine learning – Toutiao’s flagship value proposition to its users, having its own dedicated AI Lab in order to constantly further the development of the AI technology that underpins its platform. Toutiao’s algorithms learn from the types of content its users interact with and the way(s) in which they interact with this content. Given that Toutiao users spend on average 76 minutes per day on the app, there’s a wealth of data for Toutiao’s algorithms to learn form and to base personalisations on.

Variety of content types to choose from – Toutiao enables its users to upload short videos, and Toutiao’s algorithms of will recommend selected videos to appropriate users (see Fig. 3). Last year, Ivideos on Toutiao were played 1.5 billion times per day, making Toutiao China’s largest short video platform. Users can also upload pictures, similar to Instagram or Facebook, users can share their pictures, with other users being abel to like or comment on this content (see Fig. 4).

Third party integrations – Toutiao has got strategic partnerships in place with the likes of WeChat, a highly popular messaging app (see Fig. 5), and jd.com, a local online marketplace. It’s easy to see how Toutiao is following an approach whereby they’re inserting their news feed into a user’s broader ecosystem.

Main learning point: I was amazed by the scale at which Toutiao operate and the levels at which its users interact with the app. We often talk about the likes of Netflix and Spotify when it comes to personalised recommendations, but with the amount of data that Toutiao gathers, I can they can create a highly tailored content experience for their users.

Grip is a London based startup that specialises in “smart event networking software”. That sounds like a relevant problem to solve, because don’t we all have a (secret) love-hate relationship with ‘networking’ at events!?

Yes, I’d love to meet with interesting people at events but I hate approaching people randomly.

Let’s have a closer look at how Grip is looking to solve this problem:

My quick summary of Grip (before using it) – I expect an app that uses clever algorithms to suggest relevant people to meet during events.

How does Grip explain itself in the first minute? – The Grip homepage describes the tedium involved in networking at events, with attendees often failing to make the connections they’d hoped for. Grip’s value proposition is to remove this tedium by unlocking “valuable connections at your event, saving attendees time and hard work. We use advanced algorithms to recommend the right people and present them in an easy swiping interface that your attendees will love.”

Getting started, what’s the process like? – Grip uses natural language processing to connect event attendees based on interest, needs and other things they’ve got in common. I liked Grip’s ability to tell an attendee not just who, but also why they should meet someone, in the form of Reasons To Meet.

Grip users will be able to tailor the real-time recommendations they get by setting their own matchmaking rules. I like the element of Grip not totally relying on machine learning, but also giving users the opportunity to feed their preferences into category rules into the Grip dashboard. This will influence the matchmaking engine in real-time and improve the future recommendations for event exhibitors, delegates and sponsors.

I can imagine that the data around users’ acceptance or rejection of Grip’s suggested matches, will help in further refining the app’s recommendations. This reminded me about the review that I did of THEO recently. THEO acts a ‘robo-advisor’ and uses machine learning to provide its users tailored investment advice.

Integrating the Grip API – Apart from the app, Grip have also got their own API, which makes it easier for companies to incorporate event matchmaking capability into their website or apps.

Main learning point: Grip is taking a significant problem for event attendees and exhibitors, and is using machine learning to solve this problem in a real-time and personalised fashion.